A Guide to Artificial Intelligence in Healthcare
A Guide to Artificial Intelligence in Healthcare
Medical Professionals, A.I. And The Art Of Medicine
About the Book
In the last couple of years, artificial intelligence moved from being a futuristic promise into a reference point for innovation. The technology also started to transform healthcare with great vigor. It’s high time to talk about what artificial intelligence brings to medicine.
- What do machine learning, deep learning, or convolutional networks mean?
- What are the most affected medical fields?
- What are the greatest challenges for applying smart algorithms to healthcare settings?
- What could the future bring?
- What are the top use cases for artificial intelligence in healthcare during the next decade?
- What are the benefits of the exchange of knowledge between humans and machines?
- What social and ethical implications do we need to address?
Read The Medical Futurist’s guide to understand what you can anticipate from A.I. in healthcare and how to control this technology.
- Understand: Get a clear overview of the key concepts.
- Anticipate: Learn how to identify the opportunities and potential use cases of A.I. in healthcare and medicine.
- Control: Recognize and act upon the risks and implications of artificial intelligence.
About the Editor
Bundles that include this book
About the Contributors
An ambitious doctor of medicine and journalist covering the digital health, gaming and tech industries. Strong concentration, determination and attention to details are defining features. Has an insatiable interest in science, technology and video games and is always eager to gather new knowledge.
Editor in Chief, The Medical Futurist
Table of Contents
Part I. THE BASICS OF ARTIFICIAL INTELLIGENCE
- - Artificial intelligence: a reference point for innovation
- - Fears and expectations about A.I.
- - Let the quest for balanced views on A.I. begin
- - What is Artificial Intelligence?
- - Narrow, general, or super?
- - What do you need for developing A.I.?
- - Data Analytics, Machine Learning & Deep Learning – Methods of Teaching Algorithms
- - Data in healthcare
- - A brief history and the current state of electronic medical records
- - Why do we need help from A.I. when it comes to data?
Part II. APPLYING ARTIFICIAL INTELLIGENCE IN HEALTHCARE
- 1. Health data management
- 2. Treatment pathway design
- 3. Transforming diagnostics
- 4. Health assistance and administration
- 5. Patient management
- 6. Precision medicine
- 7. Supporting pharma: drug creation and clinical trials
- 8. FDA-approved algorithms in healthcare
Part III. CHALLENGES OF ARTIFICIAL INTELLIGENCE
- - Misconceptions and overhyping
- - Technological limitations of A.I.
- - Limitations of available medical data
- - The indispensable work of data annotators
- - Judgemental datasets and A.I. bias in healthcare
- - The need to regulate A.I.
- - The ethics of A.I.
- - Could you sue diagnostic algorithms or medical robots in the future?
- - Should algorithms mimic empathy?
- - Could A.I. solve the human resources crisis in healthcare?
Part IV. MEDICAL PROFESSIONALS, A.I. AND THE ART OF MEDICINE
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